Reliability is the precursor to:

Reliability is the precursor to:

• A. Norms
• B. Measurement error
• C. Validity
• D. Reliability

Answer: (C)

Reliability and validity are about how trustworthy a measurement is. The key idea here is that you can’t rightly claim a test measures what it’s supposed to measure unless the scores are dependable. Reliability means the scores are consistent across time, across different scorers, and across items. If a test gives wildly different results every time it’s used, you don’t have a stable basis for interpreting those scores. That inconsistency blocks any solid claim about what the test is intended to measure. Validity, on the other hand, is about the meaning and accuracy of the scores—whether they actually reflect the construct you care about, such as a specific clinical trait or symptom. But to judge validity, you need to know you’re reading the score reliably. If the measurement is erratic, any inference about what the score represents becomes questionable. So reliability is a prerequisite for validity: you must have dependable data before you can establish that the test is actually measuring the intended construct. It’s also helpful to keep in mind that a test can be reliable but not valid: it can be consistently measuring something, just not the intended construct. Conversely, a test that produces highly variable results would fail reliability, and thus cannot support a claim of validity. This relationship is why validity is described as following reliability in the measurement process.

Reliability and validity are about how trustworthy a measurement is. The key idea here is that you can’t rightly claim a test measures what it’s supposed to measure unless the scores are dependable. Reliability means the scores are consistent across time, across different scorers, and across items. If a test gives wildly different results every time it’s used, you don’t have a stable basis for interpreting those scores. That inconsistency blocks any solid claim about what the test is intended to measure.

Validity, on the other hand, is about the meaning and accuracy of the scores—whether they actually reflect the construct you care about, such as a specific clinical trait or symptom. But to judge validity, you need to know you’re reading the score reliably. If the measurement is erratic, any inference about what the score represents becomes questionable. So reliability is a prerequisite for validity: you must have dependable data before you can establish that the test is actually measuring the intended construct.

It’s also helpful to keep in mind that a test can be reliable but not valid: it can be consistently measuring something, just not the intended construct. Conversely, a test that produces highly variable results would fail reliability, and thus cannot support a claim of validity. This relationship is why validity is described as following reliability in the measurement process.